1. Field of the Invention
The present invention relates generally to a user preference analysis method and device and, in particular, to a user preference analysis method and device that provide a user with customized content using preference analysis based on the user's content consumption activity.
2. Description of the Related Art
Typically, mobile devices are designed to support various applications including games, Social Network Services (SNSs), messengers, email, web browsers, e-book readers, etc. For such multipurpose devices, it is an attractive scheme to provide a user with the content customized according to the user's preferences in view of the user, service provider, and mobile manufacturer. Accordingly, studies are being conducted for providing content, which is customized based on user preference analysis.
Conventional user-preferred content analysis methods analyze a user activity log recorded in use of the mobile device when consuming content. Basically, these methods are categorized into two categories: (1) where the mobile device records the user activities (such as content-saving, content up/download, content movement, copy, web-browsing key-input pattern, and accessed content (e.g., mobile application data)) and analyzes the recorded user activity; and (2) where the mobile device scrapes a User Interface (UI) screen to detect the user interface type and installed mobile applications.
In the first method category, the mobile device extracts the user's content utilization frequency, utilization time, and content access pattern, analyzes the extracted information to identify any user-preferred subjects, and acquires user preferences based on the analysis result. The analysis result is then used to sort the content, services, and advertisements available for the mobile device and recommend content to the user.
However, these conventional user activity tracking-based preference analysis methods are restricted to collecting relatively broad-spectrum activity information. For example, when a user is browsing a webpage using a mobile device, a normal webpage is commonly presented with long scroll bars, due to small display of the mobile device. As a result, the user will often have to scroll through the content on the webpage that the user is not interested in, before locating the actual desired content.
However, the conventional methods regard all of the content provided on the webpage as the desired content of the user, which wastes time and resources by analyzing large volumes of noise content, i.e., content out of the user's interest range.
Further, the noise content degrades the reliability of the user-preferred content analysis and, as the noise increases, the reliability degradation worsens, resulting in a “Garbage In Garbage Out” effect.
Accordingly, the conventional user preference analysis methods fail to produce reliable user preferences, thereby resulting in failure of user customized content and advertisements provision services.